balance_scatter | R Documentation |
Visualizing the standardized mean differences for covariates via a scatter plot.
balance_scatter( matched_set_list, xlim = c(0, 0.8), ylim = c(0, 0.8), main = "Standardized Mean Difference of Covariates", pchs = c(2, 3), covariates, data, x.axis.label = "Before refinement", y.axis.label = "After refinement", ... )
matched_set_list |
a list of one or more |
xlim |
xlim of the scatter plot. This is the same as the |
ylim |
ylim of the scatter plot. This is the same as the |
main |
title of the scatter plot. This is the same as the |
pchs |
one or more pch indicators for the symbols on the scatter plot. You should specify a phc symbol for each matched.set you specify in matched_set_list. See |
covariates |
variables for which balance is displayed |
data |
the same time series cross sectional data set used to create the matched sets. |
x.axis.label |
x axis label |
y.axis.label |
y axis label |
... |
optional arguments to be passed to |
balance_scatter
visualizes the standardized mean differences for each covariate.
Although users can use the scatter plot in a variety of ways, it is recommended that
the x-axis refers to balance for covariates before refinement, and y-axis
refers to balance after refinement. Users can utilize parameters powered by plot
in base R to further customize the figure.
In Song Kim <insong@mit.edu>, Erik Wang <haixiao@Princeton.edu>, Adam Rauh <amrauh@umich.edu>, and Kosuke Imai <imai@harvard.edu>
# get a matched set without refinement sets0 <- PanelMatch(lag = 4, time.id = "year", unit.id = "wbcode2", treatment = "dem", refinement.method = "none", data = dem, match.missing = FALSE, covs.formula = ~ I(lag(y, 1:4)) + I(lag(tradewb, 1:4)), size.match = 5, qoi = "att", outcome.var = "y", lead = 0:4, forbid.treatment.reversal = FALSE) # get a matched set with refinement using CBPS.match, setting the # size of matched set to 5 sets1 <- PanelMatch(lag = 4, time.id = "year", unit.id = "wbcode2", treatment = "dem", refinement.method = "mahalanobis", data = dem, match.missing = FALSE, covs.formula = ~ I(lag(y, 1:4)) + I(lag(tradewb, 1:4)), size.match = 5, qoi = "att", outcome.var = "y", lead = 0:4, forbid.treatment.reversal = FALSE) # get another matched set with refinement using CBPS.weight sets2 <- PanelMatch(lag = 4, time.id = "year", unit.id = "wbcode2", treatment = "dem", refinement.method = "ps.weight", data = dem, match.missing = FALSE, covs.formula = ~ I(lag(y, 1:4)) + I(lag(tradewb, 1:4)), size.match = 10, qoi = "att", outcome.var = "y", lead = 0:4, forbid.treatment.reversal = FALSE) # use the function to produce the scatter plot balance_scatter(non_refined_set = sets0$att, matched_set_list = list(sets1$att, sets2$att), data = dem, covariates = c("y", "tradewb")) # add legend legend(x = 0, y = 0.8, legend = c("mahalanobis", "PS weighting"), y.intersp = 0.65, x.intersp = 0.3, xjust = 0, pch = c(1, 3), pt.cex = 1, bty = "n", ncol = 1, cex = 1, bg = "white")
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